






"""simple docstring"""


import unittest

from transformers import AutoConfig, AutoTokenizer, BertConfig, TensorType, is_flax_available
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER, require_flax, slow


if is_flax_available():
		import jax

		from transformers.models.auto.modeling_flax_auto import FlaxAutoModel
		from transformers.models.bert.modeling_flax_bert import FlaxBertModel
		from transformers.models.roberta.modeling_flax_roberta import FlaxRobertaModel




@require_flax
class 						__UpperCamelCase     (     unittest.TestCase						):
					@slow
					def        UpperCAmelCase__ (							self							:	str   )	->       List[str]:
											for model_name in ["bert-base-cased", "bert-large-uncased"]:
																	with self.subTest(UpperCAmelCase   ):
																							lowerCAmelCase   :List[Any]						=			AutoConfig.from_pretrained(UpperCAmelCase   )
																							self.assertIsNotNone(UpperCAmelCase   )
																							self.assertIsInstance(UpperCAmelCase     , UpperCAmelCase   )

																							lowerCAmelCase   :int						=			FlaxAutoModel.from_pretrained(UpperCAmelCase   )
																							self.assertIsNotNone(UpperCAmelCase   )
																							self.assertIsInstance(UpperCAmelCase     , UpperCAmelCase   )
					@slow
					def        UpperCAmelCase__ (							self							:	Tuple   )	->       Any:
											for model_name in ["roberta-base", "roberta-large"]:
																	with self.subTest(UpperCAmelCase   ):
																							lowerCAmelCase   :Optional[Any]						=			AutoConfig.from_pretrained(UpperCAmelCase   )
																							self.assertIsNotNone(UpperCAmelCase   )
																							self.assertIsInstance(UpperCAmelCase     , UpperCAmelCase   )

																							lowerCAmelCase   :List[Any]						=			FlaxAutoModel.from_pretrained(UpperCAmelCase   )
																							self.assertIsNotNone(UpperCAmelCase   )
																							self.assertIsInstance(UpperCAmelCase     , UpperCAmelCase   )
					@slow
					def        UpperCAmelCase__ (							self							:	List[Any]   )	->       List[str]:
											for model_name in ["bert-base-cased", "bert-large-uncased"]:
																	lowerCAmelCase   :List[Any]						=			AutoTokenizer.from_pretrained(UpperCAmelCase   )
																	lowerCAmelCase   :int						=			FlaxBertModel.from_pretrained(UpperCAmelCase   )
																	lowerCAmelCase   :Any						=			tokenizer('Do you support jax jitted function?'     , return_tensors=TensorType.JAX   )

																	@jax.jit
																	def eval(**UpperCAmelCase							:	List[str]   ):
																							return model(**UpperCAmelCase   )

																	eval(**UpperCAmelCase   ).block_until_ready()
					@slow
					def        UpperCAmelCase__ (							self							:	Tuple   )	->       Dict:
											for model_name in ["roberta-base", "roberta-large"]:
																	lowerCAmelCase   :Optional[Any]						=			AutoTokenizer.from_pretrained(UpperCAmelCase   )
																	lowerCAmelCase   :Tuple						=			FlaxRobertaModel.from_pretrained(UpperCAmelCase   )
																	lowerCAmelCase   :Optional[int]						=			tokenizer('Do you support jax jitted function?'     , return_tensors=TensorType.JAX   )

																	@jax.jit
																	def eval(**UpperCAmelCase							:	Optional[Any]   ):
																							return model(**UpperCAmelCase   )

																	eval(**UpperCAmelCase   ).block_until_ready()
					def        UpperCAmelCase__ (							self							:	int   )	->       Any:
											with self.assertRaisesRegex(
											    UpperCAmelCase     , 'bert-base is not a local folder and is not a valid model identifier'   ):
																	lowerCAmelCase   :List[str]						=			FlaxAutoModel.from_pretrained('bert-base'   )
					def        UpperCAmelCase__ (							self							:	Dict   )	->       List[str]:
											with self.assertRaisesRegex(
											    UpperCAmelCase     , r'aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)'   ):
																	lowerCAmelCase   :Dict						=			FlaxAutoModel.from_pretrained(UpperCAmelCase     , revision='aaaaaa'   )
					def        UpperCAmelCase__ (							self							:	Tuple   )	->       Union[str, Any]:
											with self.assertRaisesRegex(
											    UpperCAmelCase     , 'hf-internal-testing/config-no-model does not appear to have a file named flax_model.msgpack'     , ):
																	lowerCAmelCase   :Union[str, Any]						=			FlaxAutoModel.from_pretrained('hf-internal-testing/config-no-model'   )
					def        UpperCAmelCase__ (							self							:	List[Any]   )	->       List[str]:
											with self.assertRaisesRegex(UpperCAmelCase     , 'Use `from_pt=True` to load this model'   ):
																	lowerCAmelCase   :Union[str, Any]						=			FlaxAutoModel.from_pretrained('hf-internal-testing/tiny-bert-pt-only'   )







"""simple docstring"""


import darl  # noqa
import gym
import tqdm
from diffusers.experimental import ValueGuidedRLPipeline


__SCREAMING_SNAKE_CASE   =   {
    'n_samples': 64,
    'horizon': 32,
    'num_inference_steps': 20,
    'n_guide_steps': 2,  # can set to 0 for faster sampling, does not use value network
    'scale_grad_by_std': True,
    'scale': 0.1,
    'eta': 0.0,
    't_grad_cutoff': 2,
    'device': 'cpu',
}


if __name__ == "__main__":
		__SCREAMING_SNAKE_CASE   =   'hopper-medium-v2'
		__SCREAMING_SNAKE_CASE   =   gym.make(env_name)

		__SCREAMING_SNAKE_CASE   =   ValueGuidedRLPipeline.from_pretrained(
		    'bglick13/hopper-medium-v2-value-function-hor32',
		    env=env,
		)

		env.seed(0)
		__SCREAMING_SNAKE_CASE   =   env.reset()
		__SCREAMING_SNAKE_CASE   =   0
		__SCREAMING_SNAKE_CASE   =   0
		__SCREAMING_SNAKE_CASE   =   10_00
		__SCREAMING_SNAKE_CASE   =   [obs.copy()]
		try:
				for t in tqdm.tqdm(range(T)):
						# call the policy
						__SCREAMING_SNAKE_CASE   =   pipeline(obs, planning_horizon=32)

						# execute action in environment
						__SCREAMING_SNAKE_CASE,						__SCREAMING_SNAKE_CASE,						__SCREAMING_SNAKE_CASE,						__SCREAMING_SNAKE_CASE   =   env.step(denorm_actions)
						__SCREAMING_SNAKE_CASE   =   env.get_normalized_score(total_reward)

						# update return
						total_reward += reward
						total_score += score
						print(
						    F"""Step: {t}, Reward: {reward}, Total Reward: {total_reward}, Score: {score}, Total Score:"""
						    F""" {total_score}"""
						)

						# save observations for rendering
						rollout.append(next_observation.copy())

						__SCREAMING_SNAKE_CASE   =   next_observation
		except KeyboardInterrupt:
				pass

		print(F"""Total reward: {total_reward}""")